The present invention relates to a waveform equalizer for use in a memory device for storing and reproducing data optically, magnetically or magneto-optically. More particularly, it relates to a waveform equalizer that can effectively eliminate the adverse effects of linear and nonlinear distortions.
The entire contents of Japanese Patent Application No. 8-259866 filed on Sep. 30, 1996 are incorporated herein for reference.
Conventional memory devices include optical disk devices, magnetic disk devices and magneto-optical disk devices. An optical disk device stores and reproduces data optically. A magnetic disk device stores and reproduces data magnetically. A magneto-optical disk device stores and reproduces data magneto-optically. These devices each comprise a reading head (a magnetic head or an optical pickup), a preamplifier, a waveform equalizer, a data detection circuit, and a decoder.
Each of these devices reproduce data in the following way. The reading head reads data signals from a disk. The pre-amplifier amplifies the data signals. The waveform equalizer equalizes the waveform of each data signal. The detection circuit detects binary data from the data signals whose waveforms have been equalized. The decoder decodes the binary data.
In recent years, a signal processing technique, generally known as PRML (Partial Response Maximum Likelihood) has come into use, for reproducing data signals which are recorded on a disk in high density. PRML is a technique for detecting data on the basis of the correlation of the waveforms of data signals sampled at discrete sampling points, at either the input of the waveform equalizer or the input of the data detection circuit (viterbi decoder). More precisely, the amplitude of each signal read from the disk is quantized by an A/D converter, providing a digital data item. The digital data item is subsequently processed.
The waveform equalizer is used to eliminate the distortions in the waveform of a data signal. The distortions are attributable to the characteristics of the system (particular, the recording channel) which has recorded the data on the disk. Hence, the waveform equalizer reduces the ratio of data-detection error to a value which falls within an allowable range. Known waveform equalizers each have an analog filter. Recently, an adaptive digital filter is used more often, in place of the analog filter, as data signals are now processed in the form of digital data. The adaptive digital filter renders the equalizer more adaptable to changes in the characteristics of the recording channel. (The characteristics of the recording channel depend on the recording site, e.g., track position, and on other factors.) The fundamental function of a waveform equalizer is to convert a recording/reproducing channel to a linear model and to eliminate linear distortions in the waveform of a data reproduced signal.
There has been an increasing demand for memory devices having a large memory capacity. A high recording density of several giga bits per square inch has been achieved for hard disk devices to meet the demand. However, with such a high data recording density, it is impossible to provide a linear model for the binary data recorded on the disk due to the inadequate response characteristic of the recording channel. Thus, it is still necessary to take into consideration nonlinear factors of binary data. With known linear waveform equalizers, the residual equalization errors increase when the ratio of the nonlinear distortions increases in the waveform of the reproduced signal. Consequently, it is difficult to maintain the error ratio of the detected data within an allowable range.
It will be described in detail how linear distortions and nonlinear distortions are equalized.
In a conventional disk device, the system comprising a set of a recording medium, a recording/reproducing head and other components can be regarded as a recording channel. The reproduced and isolated response waveform that corresponds to "1" of the binary data to be recorded can be assumed to be constant and remain unchanged regardless of the recording pattern. Therefore, the reproduced waveform that corresponds to the recorded binary data can be obtained by convolution of the isolated response waveform and a binary data pattern.
Since a recording channel has only a limited frequency responsiveness, an isolated response waveform has broad outskirts with a limited frequency band. The broad outskirts interferes with those of the waveforms of adjacent bits that correspond to the recorded data pattern. This gives rise to distorted waveforms. A waveform equalizer is a circuit which eliminates the interference of waveforms, thereby compensating the degraded band of the recording channel and restoring a waveform free from distortions. The waveform equalizer incorporated in a partial response type read channel reduces the waveform interference to a level that is permissible in a PR-class waveform equalizer.
The transfer characteristic of a waveform equalizer can be determined by performing linear computational operations on the response waveform of the recording channel and an ideal waveform to be applied to the detector. This known process of equalizing the distortions in the waveform of a reproduced signal, generated by the interference of waveforms that occurs when isolated waveforms are superposed will be referred to as a linear waveform equalizing process.
In the case of high-density recording of several giga bits per square inch, the nonlinear distortions are remarkable because they are inherent in systems comprising a recording medium and a recording/reproducing head. Therefore, the assumption that the reproduced and isolated response waveform is constant and remains unchanged regardless of the recording pattern does not remain true. The reproduced signal waveform is more distorted due to the shifts of the waveform, the peak position jitter and pulse width variation, all dependent on the recording pattern and unable to be determined by linear computational operations. Hence, the distortions that occur due to these factors cannot be eliminated by a known waveform equalizer. They can be eliminated only by means of a waveform treating process designed to operate in accordance with the recording pattern. A waveform equalizing process not relying upon superposing isolated single waveforms will be hereinafter referred to as a nonlinear equalizing process.
In a waveform equalizer designed to processes recorded data before the data is detected, the recorded data to be reproduced has to be selected only by presumption. Techniques of selecting recorded data consist of the use of a feedback circuit comprising a decision feedback type preliminary discriminator and the use of a learning method of combining an inter-node connection weighting factor and a threshold value determining node in a multilayer perceptron (MLP) type neural network.
However, it is difficult for a decision feedback type waveform equalizer to reproduce reliable data when the error propagation rate is high. This is because this waveform equalizer is designed to feedback a value obtained at a latter stage of the circuit. Known documents describing a nonlinear waveform equalizer having an MLP type neural network configuration (hereinafter referred to as MLP waveform equalizer) include "Nonlinear Equalization for Data Storage Channels, by Saphotharan K Nair and Jaekyun Moon, Proceedings of IEEE Int'l. Conf. Comm, New Orleans, La., USA, May 1994".
The most simple MLP type waveform equalizer has a three-layered structure. The first layer is an input layer comprising a plurality of delay elements having a delay time equal to the data clock period and connected in series. The second layer comprises a plurality of intermediate nodes. The nodes are so designed as to receive the input of the leading delay element and the outputs of the delay circuits multiplied by respective connection weights as inputs and produce as an output a value obtained by using a nonlinear function in response to the total sum of the inputs. The third layer comprises output nodes which receive the outputs of the intermediate nodes multiplied by respective connection weights as inputs and produces as an output the total sum of the inputs or a value obtained by using a nonlinear function in response to the total sum of the inputs. The connection weights used for the input layer and the nodes are determined by a technique using a backpropagation training algorithm. Known papers describing an error back propagation algorithm include "Learning internal representations by error propagation, Parallel Distributed Processing: Explorations in the Microstructure of Cognition, by Rumelhart, pp. 318-362, MIT Press, Cambridge Mass., 1986". Moon et al. "Simplified Nonlinear Equalizers, IEEE Trans. Magn., Vol. 31, No. 6, November 1995" described that the use of an MLP equalizer is effective for equalizing nonlinear distortion components.
As described earlier, the nonlinear components of the distortions contained in a reproduced signal waveform are too large to neglect, when data is reproduced from the recording medium which has a high recording density. Therefore, a nonlinear equalization process of removing the nonlinear components of the distortions is now increasingly important. Nonetheless, it still remains necessary and important to equalize the linear components of the distortions.
In view of the ever increasing recording density, it is necessary to equalize all components of the distortions of a reproduced signal waveform, both linear and nonlinear, in a data reproducing operation. While an MLP waveform equalizer provides a reliable method of equalizing linear components, using a learning effect, known linear waveform equalizers are more effective and efficient in terms of equalizing linear components and their simple circuit configuration. In fact, when a reproduced signal waveform having both linear components and nonlinear components is equalized by a three-layered MLP waveform equalizer, the nonlinear aspect of the effect of the circuit is by far unsatisfactory as will be described below.
FIGS. 1A through 5B illustrate the results of linear and nonlinear equalizing operations, obtained by simulation. FIG. 1A shows the waveform of an input signal reproduced from an optical disk storing signals which are modulated in the form of (2, 7) RLL codes and which are recorded by an NRZI (non-return-to-zero-inverted recording) system in a high density. The waveform is a synthesized one that contains no nonlinear components of distortions. FIG. 1B shows the waveform of FIG. 1A which is compressed along the time axis. FIGS. 2A and 2B shows the waveform of a signal reproduced from the same optical disk having a high recording density but containing nonlinear components of distortions. FIG. 3 shows a PR1221 type waveform obtained by equalizing the waveform of FIG. 1B by means of a 30-tap FIR type linear equalizer, the waveform containing no nonlinear components of distortions. PR1221 refers to an equalization method used for PR (partial response) signal processing operations in order to obtain a value of "0, 1, 2, 2, 1, 0" for an isolated response waveform. FIGS. 5A and 5B illustrate a signal equalized to show an ideal PR1221 waveform in two different formats.
FIG. 4A is a graph obtained by equalizing the waveform FIG. 2B to a PR1221 type waveform also by means of a 30-tap FIR type linear equalizer, the waveform containing nonlinear components of distortions. It will be seen that the nonlinear components of distortions are remarkably reflected as equalization errors. FIG. 4B is a graph obtained by equalizing the waveform of FIG. 2B by an MLP waveform equalizer. This waveform contains nonlinear components of distortions. The difference between the two waveforms is seen from the graphs. Nonetheless, the result of mean square error calculations clearly indicates that the waveform of FIG. 4B obtained by an MLP waveform equalizer is more free from equalization errors than the waveform of FIG. 4A obtained by means of an FIR type linear waveform equalizer, though the nonlinear equalization effect of an MLP waveform equalizer is not so much exploited than in the waveform of FIG. 3.
From the above, it will be understood that the learning effect of a neural network does not work so as well for an MLP waveform equalizer as it does for equalizing nonlinear distortions, when the super-position of linear adjacent waveforms and nonlinear components of distortions provide compounded factors hindering an ideal equalization target from being achieved, though the MLP waveform equalizer has a proven effect on equalizing nonlinear distortions.